Updated scores calculations

app.py

@@ -1,5 +1,4 @@
 import base64
-import io
 import json
 import os
 import uuid
@@ -10,7 +9,7 @@ import pandas as pd
 import pytz
 import streamlit as st
 from datasets import Dataset, load_dataset
-from huggingface_hub import CommitScheduler, HfApi
+from huggingface_hub import CommitScheduler
 
 # File paths as constants
 PREDICTIONS_CSV = 'dis_predictions.csv'
@@ -422,7 +421,6 @@ def fetch_latest_predictions(match_id):
 
 
 def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, outcome_only=False):
-
     # Load existing match outcomes
     outcomes = load_dataset("Jay-Rajput/DIS_IPL_Outcomes", split="train")  
     outcomes_df = pd.DataFrame(outcomes)
@@ -449,23 +447,28 @@ def update_leaderboard_and_outcomes(match_id, winning_team, man_of_the_match, ou
         users_df = pd.DataFrame(users)
 
         # Update user points based on prediction accuracy
-        for idx, prediction in predictions.iterrows():
-            user_name = prediction['user_name']
-            predicted_winner = prediction['predicted_winner']
-            predicted_motm = prediction['predicted_motm']
-            bid_points = prediction['bid_points']
-
-            # Update points based on prediction accuracy
-            points = users_df[user_name][0]['0']
-            if predicted_winner == winning_team:
-                points += 2000 + bid_points
-                if predicted_motm == man_of_the_match:
-                    points += 500
+        users_with_predictions = set(predictions['user_name'])
+        for user_name in users_df.columns:
+            user_points = users_df[user_name][0]['0']
+            if user_name in users_with_predictions:
+                prediction = predictions[predictions['user_name'] == user_name].iloc[0]
+                predicted_winner = prediction['predicted_winner']
+                predicted_motm = prediction['predicted_motm']
+                bid_points = prediction['bid_points']
+
+                # Update points based on prediction accuracy
+                if predicted_winner == winning_team:
+                    user_points += 2000 + bid_points
+                    if predicted_motm == man_of_the_match:
+                        user_points += 500
+                else:
+                    user_points -= 200 + bid_points
             else:
-                points -= 200 + bid_points
+                # Deduct 1000 points for not submitting a prediction
+                user_points -= 1000
 
             # Update user's points in the DataFrame
-            users_df[user_name][0]['0'] = points
+            users_df[user_name][0]['0'] = user_points
 
         users.to_json(USERS_JSON)
         updated_dataset = Dataset.from_pandas(users_df)